train.aep: Training the data using aep methods
Description
Training the data using aep methodsUsage
train.aep(x = x, y = y, DEBUG = FALSE, int = int, Gsub = Gsub, Cs = 10^(-3:3))
Arguments
x
expression data for training
y
a factor of length p comprising the class labels.
DEBUG
show debugging information in screen more or less.
int
Intersect of genes in network and gene expression profile.
Gsub
an adjacency matrix that represents the underlying biological network.
Cs
soft-margin tuning parameter of the SVM. Defaults to 10^c(-3:3).
Value
- The returned lists
- trainedThe tranined models for traning folds
- sig.genesThe differential expressed feature
References
Guo et al., Towards precise classification of cancers based on robust gene functional expression profiles. BMC Bioinformatics 2005, 6:58.See Also
See Also as cv.aep